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20172023
most citedMachine Learning for Survival Analysis: A Survey

107 citations · 314 across the 24 of their papers we have counts for

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Showing cs.LGShow all

20 papers · 1 filter

cs.LG2023★ 12 cited

Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting

Zibo Liu, Parshin Shojaee, Chandan K Reddy

There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task d…

cs.LG2023★ 6 cited

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

Hamid Ghaderi, Brandon Foreman, Amin Nayebi +3

Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions su…

cs.LG2023★ 16 cited

Transformer-based Planning for Symbolic Regression

Parshin Shojaee, Kazem Meidani, Amir Barati Farimani +1

Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR hav…

cs.LG2023

A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping

Hamid Ghaderi, Brandon Foreman, Amin Nayebi +3

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, a…

cs.LG2023★ 8 cited

Execution-based Code Generation using Deep Reinforcement Learning

Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni +1

The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable…

cs.LG2022

WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values

Amin Nayebi, Sindhu Tipirneni, Chandan K Reddy +2

Unpacking and comprehending how black-box machine learning algorithms make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictiv…